A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Predicting unregulated disinfection by-products in water distribution networks using generalized regression neural networks
Disinfection by-products (DBPs) formation in water distribution networks (WDNs) is a common type of water quality failure. A reliable DBPs modeling can be a way to prevent a water quality failure. In this study, generalized regression neural network (GRNN)-based models were developed to predict the occurrence of three unregulated DBPs i.e. dichloroacetonitrile (DCAN), trichloropropanone (TCP), and trichloronitromethane (TCNM). Water sampling data of several WDNs were used to develop models. Water quality parameters and regulated DBPs were used as predictors to models. The results were validated and verified. Besides, key predictors were identified followed by the sensitivity analysis. The coefficient of determination (R2) of GRNN-based models was >75% for DCAN and TCP; whereas for TCNM, the R2 < 45% was observed. The GRNN-based models exhibited better prediction accuracy compared with recently developed multiple linear regression models. The proposed framework can be used to develop models of other contaminants.
Predicting unregulated disinfection by-products in water distribution networks using generalized regression neural networks
Disinfection by-products (DBPs) formation in water distribution networks (WDNs) is a common type of water quality failure. A reliable DBPs modeling can be a way to prevent a water quality failure. In this study, generalized regression neural network (GRNN)-based models were developed to predict the occurrence of three unregulated DBPs i.e. dichloroacetonitrile (DCAN), trichloropropanone (TCP), and trichloronitromethane (TCNM). Water sampling data of several WDNs were used to develop models. Water quality parameters and regulated DBPs were used as predictors to models. The results were validated and verified. Besides, key predictors were identified followed by the sensitivity analysis. The coefficient of determination (R2) of GRNN-based models was >75% for DCAN and TCP; whereas for TCNM, the R2 < 45% was observed. The GRNN-based models exhibited better prediction accuracy compared with recently developed multiple linear regression models. The proposed framework can be used to develop models of other contaminants.
Predicting unregulated disinfection by-products in water distribution networks using generalized regression neural networks
Mian, Haroon R. (author) / Hu, Guangji (author) / Hewage, Kasun (author) / Rodriguez, Manuel J. (author) / Sadiq, Rehan (author)
Urban Water Journal ; 18 ; 711-724
2021-10-21
14 pages
Article (Journal)
Electronic Resource
Unknown
Site characterization using generalized regression neural networks
| British Library Conference Proceedings | 2001
| British Library Online Contents | 2012
Predicting Water Levels Using Artificial Neural Networks
| British Library Conference Proceedings | 2003